What Am I Really Listening To?

Chris Dalla Riva and I take a deep dive into my Spotify history

Photo by Sara Kurfeß on Unsplash

Good Morning!

Today we’re taking a data-driven look at Spotify. Specifically, my listening habits.

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It’s no secret that social media is mostly performative. We curate our Twitter feeds to show what we want them to show. We add filters to pictures on Instagram and only post the best ones, lest anyone think we aren’t living our best lives. I’m as guilty as anyone; I post sunrise pics from work, but never ones of my driving to work in the middle of the night. Where’s the fun in that?

Our listening habits aren’t immune— or at least what we tell people we’re playing. I always say that there is no such thing as a guilty pleasure here, and I mean it. If it moves you- or makes you move- it’s good—the end.

But…we’re human. And if you’re of a certain age, you also grew up around insufferable gatekeepers who made sure they never missed an opportunity to either demand you list out three songs by [insert band here] or tell you what you liked sucks.

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The rise of streaming- first with sketchy places like Kazaa, then iTunes, and now Spotify largely stripped these people of any agency. Listeners could listen to almost anything, almost anywhere, without worrying about getting called out. Listening also became a largely solo endeavor, but that’s a topic for another day.

The rise of these platforms also gave rise to huge data sets. Gone were the days of paper forms or even telephone calls to gauge popularity. Today, companies like Spotify can see in real-time what you’re listening to, when, and where. They use that data to weaponize their algorithms to give you more of what they think you want.

It all makes for a collision of subjective taste and objective data.

Chris Dalla Riva

lives at this intersection. As a musician, he knows all too well our emotional connection to the music we love. As someone who also works in data analytics for a music streaming service, he is trained to find the larger story/trend in the numbers. His Can’t Get Much Higher Substack goes deep on both, writing data-driven analyses on trends in the music world. It’s one of my “never miss’ newsletters because I never know where he will take the story. I highly recommend taking a second to check it out.

To be clear, these aren’t dry readings of spreadsheets. He makes data, well, fun. And takes it in quirky directions. More importantly, he packages it in an interesting and informative way. When he proposed deep diving into my Spotify history, I didn’t blink.

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It can take up to a month for Spotify to deliver your history to you, depending on how extensive it is.

While we were waiting, a few quick questions came to mind:

• I’m on a shared plan with my younger son. Would this skew the data? We live in very different music worlds, and while I’d recognize who was listening to what, would Chris be able to?

• I’ve fallen asleep (or gone to work) more than once with random stuff left on repeat. Would those spikes show up? If so, what would they look like?

• How many songs have I listened to in total?

Chris went in looking for more analytical answers:

• When was I listening?

• Was I skipping around or listening to songs/records all the way through?

• Was I playing new (to me) tracks, or was I listening to the same tracks over & over?

• What was I listening to, anyway?

Chris takes over below the jump and shares what he found.

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What Does Kevin Alexander Actually Have on Repeat?

It’s not that I don’t trust Kevin Alexander. I do. That’s why I read his newsletter faithfully. But I feel like when people describe their musical tastes, they always filter out their guilty pleasures and leave only the hippest stuff.

So, with Kevin’s blessing, I decided to check if that was the case for him. He requested his listening history from Spotify and sent it over to me. Then, I crunched the numbers.

Kevin Alexander is not a liar. You can see that from quickly scrolling through his listening history. He does indeed listen to anything he mentions in his weekly “What’re you listening to?’ threads, often many times. Furthermore, his top artists are what you’d expect, given his writing.

You have to go very deep into Kevin’s all-time top artists to find things that are truly unexpected. The only two that jumped out at me were Janet Jackson, ranked 132 by listening time and 173 by plays, and Ariana Grande, ranked 244 by listening time and 180 by plays.

Regardless, Kevin’s listening is both exploratory and deliberate. I say exploratory because 60.8% of his all-time played songs have been listened to exactly once, meaning he often tries new things. At the same time, 39.8% of songs have been played more than once, 11.5% five or more times, and 4.0% ten or more times, meaning that he is willing to listen deeply when he finds something that he enjoys.

I say that his listening is also deliberate because Kevin rarely listens on shuffle, and if he clicks play on a song, there’s a 68% chance he’ll listen to the end. He is seeking things out and isn’t that willing to have his listening dictated by algorithms and shuffled playlists.

Looking at someone else’s listening history is odd. In fact, truly understanding what someone spends their time listening to feels like glaring into their soul. Kevin’s listening history makes the passion in his musical soul obvious.

On March 9, 2012, he kicked off his Spotify listening journey by playing The Go! Team’s “Ladyflash.” He would then go on to play 80 more songs that day. The next day, he would play 98, putting those two days in his top 3.0% of listening days all-time. The data paints a picture of an audiophile truly shocked by the amount of music he now had access to for a paltry monthly sum.

I could go on describing Kevin’s musical soul. I could tell you that he almost never listens to Christmas music and that I was truly shocked to find his most played Bruce Springsteen is “Tunnel of Love.” But, like I said, if you read On Repeat, you already have a pretty good idea of what he listens to. What’s most odd about looking at someone else’s listening history is everything else you can learn about them.

For example, I know that Kevin runs Windows on his desktop, and most of his listening these days - about 83% in terms of plays and 86% in terms of listening time - occurs on that Windows machine. That wasn’t always the case, though. In the first few years of his listening, most of his plays occurred on a Mac desktop. When he listens on the go - at least since 2019 - he does it on an iPhone, but from 2015 to 2018, he opted for an iPad. No matter the device, he must have great data and Wi-Fi coverage because he seldom listens to anything offline.

Kevin’s listening history also clues us into where he spends his time. Based on the IP address associated with his plays, I think he lives somewhere in Wisconsin and spends some time regularly in Georgia.

I can also tell that he probably went on vacation to the Bahamas in January 2023. He didn’t do much listening on the trip, though. There were only six recorded plays.

• “Belinda Says by Alvvays

• “Fuori dal mondo (Keep Searchin')” by I Giganti

• “Throwing Darts at the Universe” by Stephanie Losi

• “Mandingo Cliche” by Belle and Sebastian

• “Lonely Street” by Guadalcanal Diary

• “Canyon” by JOSEPH

The one piece of data that left me scratching my head is which part of the day Kevin is listening to music. Though 80.4% of his listening time occurs after 3 PM in recent years, a notable chunk occurs between midnight and 6 AM. Given that most people are asleep at these times and that he does very little listening between 8 AM and noon, I suspect he works irregular hours.

For the record, this data isn’t that intrusive. It’s really just context for when and where the plays occurred. Though I’m sure Spotify has more data on its users that isn’t included in their standard play history report, it’s a good reminder of how much online platforms know about us. Imagining what a platform like Facebook can deduce about a user is almost dizzying, given their access to images and chat history, among other things.

Rather than turning this into a rant about big tech, I’ll play Big Brother once more and show you Kevin’s top artists of all time by letter of the alphabet. Given that he’s played multiple songs by an artist of each letter, I think it’s another great example of his listening diversity.

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Kevin here again:

I hope you had as much fun reading this as we did putting it together. Requesting your history from Spotify is fairly straightforward; unpacking it is not. Am I the only one who didn’t know what a JSON file was? Luckily, Chris did and made quick work of it all.

A few answers:

Even though my son & I are on a shared plan, our data is firewalled (again, maybe I was the only one who didn’t know that).

The times I’ve let something play on repeat did not have a meaningful impact on the stats overall.

Springsteen’s best record is Tunnel of Love, and that is a hill I will die on. The title track is my favorite.

I am definitely listening in the wee hours of the night. In real life, I work on the back side of the clock.

I have one Christmas playlist that gets played, like 2-3 max per year. Usually, once when we decorate the tree, and maybe once on Christmas Day.

Chris is right to describe me as an audiophile truly shocked by the amount of music he now had access to. In those first few days on the platform, I was like a kid in a candy store. I still am, sometimes.

“Ladyflash” is an awesome song. I should play it more, tbh.

As of the beginning of August, I was at 53,372 total plays. I know that’s probably not a lot in the grand scheme of things, but it was more than I thought it would be.

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Huge thanks to Chris for taking this on and to you for reading it! What are your thoughts on big data and platforms like Spotify collecting it? Have you ever pulled your listening history? If so, what’d you find?

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Thanks for being here,

Kevin—